class_docstr: First version of Epistemic computation used as active learning strategy
class_docstr_tok: ["First","version","of","Epistemic","computation","used","as","active","learning","strategy"]
code: def __init__(
      self,
      model,
      network=None,
      transforms=None,
      roi_size=(128, 128, 64),
      num_samples=10,
      load_strict=False,
  ):
      super().__init__("Compute initial score based on dropout")
      self.model = model
      self.network = network
      self.transforms = transforms
      self.device = "cuda" if torch.cuda.is_available() else "cpu"
      self.roi_size = roi_size
      self.num_samples = num_samples
      self.load_strict = load_strict
  
file_path: MONAILabel/monailabel/tasks/scoring/epistemic.py
imports: import copy
  import logging
  import os
  import time
  import numpy
  import torch
  from monai.inferers import sliding_window_inference
  from monai.transforms import Compose
  from monai.utils import deprecated
  from monailabel.interfaces.datastore import Datastore
  from monailabel.interfaces.tasks.scoring import ScoringMethod
label_desc: Use image node_id 1 to create a new EpistemicScoring object from inherited base classes: ScoringMethod with example: obj = EpistemicScoring(model, network, transforms, roi_size, num_samples, load_strict)